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Adaptive Compression in Federated Learning via Side Information
April 23, 2024, 4:43 a.m. | Berivan Isik, Francesco Pase, Deniz Gunduz, Sanmi Koyejo, Tsachy Weissman, Michele Zorzi
cs.LG updates on arXiv.org arxiv.org
Abstract: The high communication cost of sending model updates from the clients to the server is a significant bottleneck for scalable federated learning (FL). Among existing approaches, state-of-the-art bitrate-accuracy tradeoffs have been achieved using stochastic compression methods -- in which the client $n$ sends a sample from a client-only probability distribution $q_{\phi^{(n)}}$, and the server estimates the mean of the clients' distributions using these samples. However, such methods do not take full advantage of the FL …
abstract accuracy art arxiv client communication compression cost cs.dc cs.lg federated learning information sample scalable server state stat.ml stochastic type updates via
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